101591 Views
79842 Views
45596 Views
44332 Views
40991 Views
33419 Views
Raspberry Pi Time machine
Now Ad-Free
Guiding Light
Sync Files on your Pis, with Syncthing
NextCloud
Buddy Jr.
Introduction to FreeCAD for Beginners
Building a Robot Arm with Raspberry Pi and PCA9685
Building User Authentication for Static Sites with FastAPI
Mastering Pydantic for Robust Data Validation
Mastering Markdown for Documentation with Jekyll
Introduction to Rust
KevsRobots Learning Platform
80% Percent Complete
By Kevin McAleer, 3 Minutes
In this lesson, we focus on Visualization with Pandas and Matplotlib. Effective data visualization is crucial for interpreting and communicating data insights. We’ll explore how Pandas integrates with Matplotlib to create a variety of charts and graphs for effective data presentation.
Matplotlib is a comprehensive library for creating static, animated, and interactive visualizations in Python. Pandas data structures integrate well with Matplotlib.
Matplotlib
Pandas DataFrames and Series have a .plot() method, which is a wrapper around Matplotlib’s functionality:
.plot()
# Basic line plot df['ColumnName'].plot() # Basic histogram df['ColumnName'].plot.hist()
You can customize plots with various parameters:
# Customized plot df.plot(kind='bar', title='Bar Chart', legend=True, fontsize=12)
Compare multiple columns in a single plot:
# Plotting multiple columns df[['Column1', 'Column2']].plot(kind='bar')
Time series data can be visualized effectively:
# Time series plot time_series_df.plot()
For more advanced plots, directly use Matplotlib:
import matplotlib.pyplot as plt # Scatter plot with Matplotlib plt.scatter(df['Column1'], df['Column2']) plt.title('Scatter Plot') plt.xlabel('Column 1') plt.ylabel('Column 2') plt.show()
Matplotlib is a versatile plotting library in Python, widely used for creating static, interactive, and animated visualizations. Here are a few examples to showcase its capabilities:
import matplotlib.pyplot as plt x = [0, 1, 2, 3, 4, 5] y = [0, 1, 4, 9, 16, 25] plt.plot(x, y) plt.title('Basic Line Plot') plt.xlabel('X Axis') plt.ylabel('Y Axis') plt.show()
This code produces a simple line plot, plotting x versus y.
x
y
categories = ['Category A', 'Category B', 'Category C'] values = [5, 10, 15] plt.bar(categories, values) plt.title('Bar Chart') plt.xlabel('Categories') plt.ylabel('Values') plt.show()
This creates a bar chart displaying the values of different categories.
import numpy as np x = np.random.rand(50) y = np.random.rand(50) colors = np.random.rand(50) area = (30 * np.random.rand(50))**2 plt.scatter(x, y, s=area, c=colors, alpha=0.5) plt.title('Scatter Plot') plt.show()
This scatter plot uses random data, with varying sizes and colors for the points.
data = np.random.normal(0, 1, 1000) plt.hist(data, bins=30) plt.title('Histogram') plt.xlabel('Value') plt.ylabel('Frequency') plt.show()
This code generates a histogram of normally distributed data.
sizes = [15, 30, 45, 10] labels = ['Frogs', 'Hogs', 'Dogs', 'Logs'] plt.pie(sizes, labels=labels, autopct='%1.1f%%', startangle=140) plt.axis('equal') plt.title('Pie Chart') plt.show()
This creates a pie chart showing the relative sizes of different items.
This lesson introduced you to the basics of visualization with Pandas and Matplotlib. From simple line charts to more complex scatter plots, you now have the tools to visualize and communicate your data effectively.
< Previous Next >